
. This work has
been partly supported by the French ANR09JCJC009801
In previous work, we have introduced several fast algorithms for relaxed power series multiplication (also known under the name online multiplication) up till a given order . The fastest currently known algorithm works over an effective base field with sufficiently many th roots of unity and has algebraic time complexity . In this note, we will generalize this algorithm to the cases when is replaced by an effective ring of positive characteristic or by an effective ring of characteristic zero, which is also torsionfree as a module and comes with an additional algorithm for partial division by integers. We will also present an asymptotically faster algorithm for relaxed multiplication of adic numbers.

Let be an effective (possibly non commutative) ring. That is, we assume data structures for representing the elements of and algorithms for performing the ring operations , and . The aim of algebraic complexity theory is to study the cost of basic or more complex algebraic operations over (such as the multiplication of polynomials or matrices) in terms of the number of operations in .
The algebraic complexity usually does not coincide with the bit complexity, which also takes into account the potential growth of the actual coefficients in . Nevertheless, understanding the algebraic complexity usually constitutes a first useful step towards understanding the bit complexity. Of course, in the special case when is a finite field, both complexities coincide up to a constant factor.
One of the most central operations is polynomial multiplication. We will denote by the number of operations required to multiply two polynomials of degrees in . If admits primitive th roots of unity for all , then we have using FFT multiplication, which is based on the fast Fourier transform [CT65]. In general, it has been shown [SS71, CK91] that . The complexities of most other operations (division, Taylor shift, extended g.c.d., multipoint evaluation, interpolation, etc.) can be expressed in terms of . Often, the cost of such other operations is simply , where stands for ; see [AHU74, BP94, GG02] for some classical results along these lines.
The complexity of polynomial multiplication is fundamental for studying the cost of operations on formal power series in up to a given truncation order . Clearly, it is possible to perform the multiplication up to order in time : it suffices to multiply the truncated power series at order and truncate the result. Using Newton's method, and assuming that , it is also possible to compute , , etc. up to order in time . More generally, it has been shown in [BK78, Hoe02, Sed01, Hoe10] that the power series solutions of algebraic differential equations with coefficients in can be computed up to order in time . However, in this case, the “” hides a non trivial constant factor which depends on the size of the equation that one wants to solve.
The relaxed approach for computations with formal power series makes it possible to solve equations in quasioptimal time with respect to the sparse size of the equations. The idea is to consider power series as streams of coefficients and to require that all operations are performed “without delay” on these streams. For instance, when multiplying two power series , we require that is computed as soon as are known. Any algorithm which has this property will be called a relaxed or online algorithm for multiplication.
Given a relaxed algorithm for multiplication, it is possible to let the later coefficients of the input depend on the known coefficients of the output. For instance, given a power series with , we may compute using the formula
Indeed, extraction of the coefficient of in and yields
and only depends on . More generally, we define an equation of the form
to be recursive, if only depends on . Replacing by , we notice that the same terminology applies to systems of equations. In the case of an implicit equation, special rewriting techniques can be implied in order transform the input equation into a recursive equation [Hoe11, Hoe09, BL11].
Let denote the cost of performing a relaxed multiplication up to order . If is an expression which involves multiplications and other “linear time” operations (additions, integrations, etc.), then it follows that (2) can be solved up to order in time . If we had , then this would yield an optimal algorithm for solving (2) in the sense that the computation of the solution would essentially require the same time as its verification.
The naive algorithm for computing , based on the formula
is clearly relaxed. Unfortunately, FFT multiplication is not relaxed, since are computed simultaneously as a function of , in this case.
In [Hoe97, Hoe02] it was remarked that Karatsuba's algorithm [KO63] for multiplying polynomials can be rewritten in a relaxed manner. For small , Karatsuba multiplication and relaxed multiplication thus require exactly the same number of operations. In [Hoe97, Hoe02], an additional fast relaxed algorithm was presented with time complexity
We were recently made aware of the fact that a similar algorithm was first published in [FS74]. However, this early paper was presented in a different context of online (relaxed) multiplication of integers (instead of power series), and without the application to the resolution of recursive equations (which is quite crucial from our perspective).
An interesting question remained: can the bound (3) be lowered further, be it by a constant factor? In [Hoe03], it was first noticed that an approximate factor of two can be gained if one of the multiplicands is known beforehand. For instance, if we want to compute for a known series with , then the coefficients of are already known in the product in (1), so only one of the inputs depends on the output. An algorithm for the computation of is said to be semirelaxed, if is written to the output as soon as are known, but all coefficients of are known beforehand. We will denote by the complexity of semirelaxed multiplication. We recall from [Hoe07] (see also section 3) that relaxed multiplication reduces to semirelaxed multiplication
The first reduction of (3) by a non constant factor was published in [Hoe07], and uses the technique of FFT blocking (which has also been used for the multiplication of multivariate polynomials and power series in [Hoe02, Section 6.3], and for speeding up Newton iterations in [Ber00, Hoe10]). Under the assumption that admits primitive th roots of unity for all (or at least for all with ), we showed that
Since this complexity will play a central role in the remainder of this paper, it will be convenient to abbreviate
The function has slower growth than any strictly positive power of . It will also be convenient to write whenever for all . In particular, it follows that
In section 3, we will recall the main ideas from [Hoe07] which lead to the complexity bound (4).
We recall that the characteristic of a ring is the integer such that the canonical ring homomorphism has kernel . If is torsionfree as a module, then we will say that admits an effective partial division by integers if there exists an algorithm which takes and on input and which returns the unique with on output. The main result of this paper is:
is an effective ring of characteristic zero, which is torsionfree as a module, and which admits an effective partial division by integers.
is an effective ring of positive characteristic.
Then we have
We notice that the theorem holds in particular if is an effective field. In Section 7, we will also consider the relaxed multiplication of adic numbers, with and . If we denote by the bit complexity of multiplying two bit integers, then [FS74] essentially provided an algorithm of bit complexity for the relaxed multiplication of adic numbers at order . Various algorithms and benchmarks for more general were presented in [BHL11]. It is also well known [Too63, Coo66, SS71, Für07] that . Let denote the bit complexity of the relaxed multiplication of two adic numbers modulo . In Section 7, we will prove the following new result:
The main idea which allows for the present generalizations is quite straightforward. In our original algorithm from [Hoe07], the presence of sufficiently many primitive th roots of unity in gives rise to an quasioptimal evaluationinterpolation strategy for the multiplication of polynomials. More precisely, given two polynomials of degrees , their FFTmultiplication only requires evaluation and interpolation points, and both the evaluation and interpolation steps can be performed fast, using only operations. Now it has recently been shown [BS05] that quasioptimal evaluationinterpolation strategies still exist if we evaluate and interpolate at points in geometric progressions instead of roots of unity. This result is the key to our new complexity bounds, although further technical details have to be dealt with in order to make things work for various types of effective rings . We also notice that the main novelty of [BS05] concerns the interpolation step. Fast evaluation at geometric progressions was possible before using the so called chirp transform [RSR69, Blu70]. For effective rings of small characteristic , this would actually have been sufficient for the proving the bound (5).
Our paper is structured as follows. Since the algorithms of [BS05] were presented in the case when is an effective field, Section 2 starts with their generalization to more general effective rings . These generalizations are purely formal and contain no essentially new ideas. In Section 3, we give a short survey of the algorithm from [Hoe07], but we recommentd the reader to download the original paper from our webpage for full technical details. In Section 4, we prove Theorem 1 in the case when has characteristic zero. In Section 5, we turn our attention to the case when has prime characteristic . If the characteristic is sufficiently large, then we may find sufficiently large geometric progressions in in order to generalize the results from Section 4. Otherwise, we have to work over for some sufficiently large . In Section 6, we complete the prove of Theorem 1; the case when is a prime power is a refinement of the result from Section 5. The remaining case is done via Chinese remaindering. In Section 7, we will prove Theorem 2.
Let be an effective integral domain and let be its quotient field. Assume that has characteristic zero. Let be an effective torsionfree module and . Elements of and are fractions with (resp. ) and , and the operations on such fractions are as usual:
For , we also have . It follows that and are effective fields and vector spaces. Moreover, all field operations in (and all vector space operations in ) can be performed using only operations in (resp. or ).
We will say that admits an effective partial division, if for every and , we can compute the unique with . From now on, we will assume that this is the case, and we will count any division of the above kind as one operation in . Given , we define
Given and , we will denote by the number of operations in and which are needed in order to compute the product .
We may compute from using operations in and .
We may reconstruct from using operations in and .
Proof. In the case when is a field of characteristic zero and , this result was first proven in [BS05]. More precisely, the conversions can be done using the algorithms NewtonEvalGeom, NewtonInterpGeom, NewtonToMonomialGeom and MonomialToNewtonGeom in that paper. Examining these algorithms, we observe that general elements in are only multiplied with elements in and divided by elements of the set . In particular, the algorithms can still be applied in the more general case when is a field of characteristic zero and a vector space.
If is only an effective commutative integral domain of characteristic zero and an effective torsionfree module with an effective partial division, then we define the effective field and the effective vector field as above, and we may still apply the generalized algorithms for multipoint evaluation and interpolation in . In particular, both multipoint evaluation and interpolation can still be done using operations in and , whence operations in and . If we know that the endresults of these algorithms are really in the subspace of (or in the subring of ), then we use the partial division in to replace their representations in (or ) by representations in (or ).
We may compute from using operations in and .
We may reconstruct from using operations in and .
Proof. We again use straightforward generalizations of the algorithms in [BS05], using the fact that we only divide by elements in the set .
Let be an effective (possibly non commutative) ring and recall that
Given a power series and , we will also use the notations
The fast relaxed algorithms from [Hoe07] are all based on two main changes of representation: “blocking” and the fast Fourier transform. Let us briefly recall these transformations and how to use them for the design of fast algorithms for relaxed multiplication.
Given , we may then compute using
where and
and it is classical [CT65] that both and can be computed using operations in . The operations and extend naturally to via
This allows us to compute using the formula
The first coefficients of can be computed using at most operations in .
allows for the relaxed computation of at order using at most
operations in . Similarly, the formula
allows for the semirelaxed computation of at order using at most
operations in . For a given expansion order , one may take , and use the above formula in a recursive manner. This yields [Hoe07, Theorem 11]
Remark
shows that a relaxed product of two polynomials and of degrees reduces to a relaxed product of half the size, two semirelaxed products , , and one nonrelaxed product . Under the assumptions that and are increasing, a routine calculation thus yields
and the complexity bound
For and well chosen block sizes (depending on the expansion order ), the recursive application of this technique yields [Hoe07, Theorem 12]
Let us now consider the less favourable case when is an effective ring which does not necessarily contain primitive th roots of unity for arbitrarily high . In this section, we will first consider the case when is torsionfree as a module and also admits a partial algorithm for division by integers.
Given a block size and (say ), we will replace the discrete Fourier transform at a th primitive root of unity by multipoint evaluation at . More precisely, we define
and the inverse transform . By Lemma 3, these transforms can both be computed using operations in . In a similar way as and , we extend and to power series in .
Proof. It suffices to prove the complexity bound for semirelaxed multiplication. We adapt the multiplication algorithm with different block sizes from the end of the previous section, and replace (7) by
This leads to the complexity bound
We now follow the proof of [Hoe07, Theorem 12]. Denote
and take . Using that , this leads to
Applying this relation times, we obtain
Taking
this yields
Reexpressing this bound in terms of yields the desired result.
Let now be an effective ring of prime characteristic . For expansion orders , the ring does not necessarily contain distinct points in geometric progression. Therefore, in order to apply Lemma 4, we will first replace by a suitable extension, in which we can find sufficiently large geometric progressions.
Given , let be even such that . Let be such that the finite field is isomorphic to . Then the ring
has dimension over as a vector space, so we have a natural linear bijection
The ring is an effective ring and one addition or subtraction in corresponds to additions or subtractions in . Similarly, one multiplication in can be done using operations in .
In order to multiply two series up to order , the idea is now to rewrite and as series in with . If we want to compute the relaxed product, then we also have to treat the first coefficients apart, as we did before for the blocking strategy. More precisely, we will compute the semirelaxed product using the formula
where we extended to in the natural way:
From the complexity point of view, we get
Since contains a copy of , it also contains at least points in geometric progression. For the multiplication up till order of two series with coefficients in , we may thus use the blocking strategy combined with multipoint evaluation and interpolation.
Proof. With the notations from above, we may find a primitive th root of unity in . We may thus use formula (9) for the semirelaxed multiplication of two series in up till order . In a similar way as in the proof of Theorem 6, we thus get
Using classical fast relaxed multiplication [Hoe97, Hoe02, FS74], we also have
whence (11) simplifies to
Since and , the result follows.
Remark
If we insist on computing in a deterministic way, then it is better to slightly change our algorithm, and systematically choose to be a prime number minus one. Under this assumption, the cyclotomic polynomial is irreducible over ; see [GG02, Lemma 14.50]. For a fixed , the first prime number with still has size , by the prime number theorem, and we may compute it in time using the sieve of Eratosthenes. This again shows that a suitable can be precomputed with negligible cost.
Let us now show that the technique from the previous section actually extends to the case when is an arbitrary effective ring of positive characteristic. We first show that the algorithm still applies when the characteristic of is a prime power. We then conclude by showing how to apply Chinese remaindering in our setting.
Proof. Taking , let be as in the previous section and pick a monic polynomial in such that the reduction of modulo yields . Then we get a natural commutative diagram
where stands for reduction modulo . In particular, we have an epimorphism
with .
Now let be an element in of order . Then any lift of with has order at least . Moreover, and are all invertible. Consequently, and do not lie in , whence they are invertible as well. It follows that we may still apply multipoint evaluation and interpolation in at the sequence , whence Theorem 7 generalizes to the present case.
Remark
Proof. We will prove the theorem by induction on the number of prime divisors of . If is a prime power, then we are done. So assume that , where and are relatively prime, and let be such that
Then we may consider the rings
These rings are effective, when representing their elements by elements of and transporting the operations from . Of course, the representation of an element of (or ) is not unique, since we may replace it by for any (or ). But this is not a problem, since our definition of effective ring did not require unique representability or the existence of an equality test.
Now let and let be their projections in , for . Consider the relaxed products , for . These products are represented by relaxed series via , for . By the induction hypotheses, we may compute and at order using
operations in . The linear combination can still be expanded up till order with the same complexity. We claim that . Indeed,
Summing both relations, our claim follows.
Let be an integer, not necessarily a prime number, and denote . We will regard adic numbers as series with , and such that the basic ring operations , and require an additional carry treatment.
In order to multiply two relaxed adic numbers , we may rewrite them as series , multiply these series , and recover the product from the result. Of course, the coefficients of may exceed , so some relaxed carry handling is required in order to recover from . We refer to [BHL11, Section 2.7] for details. In particular, we prove there that can be computed up to order using ring operations in of bit size .
Given , let , and consider two power series . We will denote by (resp. ) the bit complexity of multiplying and up to order using a relaxed (resp. semirelaxed) algorithm.
Proof. Let and let be a prime number (in practice, we recommend to take to be a prime number which fits inside one machine word and such that is divisible by a high power of two). Let be sufficiently large such that . Let be the reductions of modulo . Then may be reconstructed up to order from the product . We thus get
By Theorem 9, we have
By Remark 10, this bound is uniform in . Since , the result follows.
For the above strategy to be efficient, it is important that . This can be achieved by combining it with the technique of adic blocking. More precisely, given a adic block size , then any adic number in can naturally be considered as a adic number in , and vice versa. Assuming that numbers in are written in base , the conversion is trivial if is a power of two. Otherwise, the conversion involves base conversions and we refer to [BHL11, Section 4] for more details. In particular, the conversions in both directions up to order can be done in time .
Let (resp. ) the complexity of relaxed (resp. semirelaxed) multiplication in up till order .
Proof. Let , so that
Using the strategy of adic blocking, a semirelaxed product in may then be reduced to one semirelaxed product in and one relaxed multiplication with an integer in . In other words,
where stands for the cost of semirelaxed multiplication of two adic numbers in up till order . By [BHL11, Proposition 4], we have
By Lemma 12, we also have
In particular,
which completes the proof of the theorem.
Remark
We thus improved the previous bound by a factor at least, up to sublogarithmic terms.
For the moment, we have not implemented any of the new algorithms in practice. Nevertheless, our old implementation of the algorithm from [Hoe07] allowed us to gain some insight on the practical usefulness of blockwise relaxed multiplication. Let us briefly discuss the potential impact of the new results for practical purposes.
In the case of integer coefficients, it is best to reencode the integers as polynomials in for a prime number which fits into a machine word and such that admits many th roots of unity (it is also possible to take several primes and use Chinese remaindering). After that, one may again use the old algorithm from [Hoe07]. Also, integer coefficients usually grow in size with , so one really should see the power series as a bivariate power series in with a triangular support. One may then want to use TFTstyle multiplication [Hoe04, Hoe05] in order to gain another constant factor.
If denotes the cost of multiplying two polynomials and in with , then the classical fast relaxed multiplication algorithm from [Hoe02, FS74] generalizes and still admits the time complexity . However, the blockwise algorithm from this paper does not generalize to this setting, at least not in a straightforward way.
A. Aho, J. Hopcroft, and J. Ullman. The Design and Analysis of Computer Algorithms. AddisonWesley, Reading, Massachusetts, 1974.
D. Bernstein. Removing redundancy in high precision Newton iteration. Available from http://cr.yp.to/fastnewton.html, 2000.
J. Berthomieu, J. van der Hoeven, and G. Lecerf. Relaxed algorithms for adic numbers. Journal de Théorie des Nombres de Bordeaux, 23(3):541–577, 2011.
R.P. Brent and H.T. Kung. Fast algorithms for manipulating formal power series. Journal of the ACM, 25:581–595, 1978.
J. Berthomieu and R. Lebreton. Relaxed adic hensel lifting for algebraic systems. Work in preparation, 2011.
L.I. Bluestein. A linear filtering approach to the computation of the discrete fourier transform. IEEE Trans. Electroacoustics, AU18:451–455, 1970.
D. Bini and V.Y. Pan. Polynomial and matrix computations. Vol. 1. Birkhäuser Boston Inc., Boston, MA, 1994. Fundamental algorithms.
A. Bostan and É. Schost. Polynomial evaluation and interpolation on special sets of points. Journal of Complexity, 21(4):420–446, August 2005. Festschrift for the 70th Birthday of Arnold Schönhage.
D.G. Cantor and E. Kaltofen. On fast multiplication of polynomials over arbitrary algebras. Acta Informatica, 28:693–701, 1991.
S.A. Cook. On the minimum computation time of functions. PhD thesis, Harvard University, 1966.
J.W. Cooley and J.W. Tukey. An algorithm for the machine calculation of complex Fourier series. Math. Computat., 19:297–301, 1965.
M.J. Fischer and L.J. Stockmeyer. Fast online integer multiplication. Proc. 5th ACM Symposium on Theory of Computing, 9:67–72, 1974.
M. Fürer. Faster integer multiplication. In Proceedings of the ThirtyNinth ACM Symposium on Theory of Computing (STOC 2007), pages 57–66, San Diego, California, 2007.
J. von zur Gathen and J. Gerhard. Modern Computer Algebra. Cambridge University Press, 2nd edition, 2002.
J. van der Hoeven. Lazy multiplication of formal power series. In W. W. Küchlin, editor, Proc. ISSAC '97, pages 17–20, Maui, Hawaii, July 1997.
J. van der Hoeven. Relax, but don't be too lazy. JSC, 34:479–542, 2002.
J. van der Hoeven. Relaxed multiplication using the middle product. In Manuel Bronstein, editor, Proc. ISSAC '03, pages 143–147, Philadelphia, USA, August 2003.
J. van der Hoeven. The truncated Fourier transform and applications. In J. Gutierrez, editor, Proc. ISSAC 2004, pages 290–296, Univ. of Cantabria, Santander, Spain, July 4–7 2004.
J. van der Hoeven. Notes on the Truncated Fourier Transform. Technical Report 20055, Université ParisSud, Orsay, France, 2005.
J. van der Hoeven. New algorithms for relaxed multiplication. JSC, 42(8):792–802, 2007.
J. van der Hoeven. Relaxed resolution of implicit equations. Technical report, HAL, 2009. http://hal.archivesouvertes.fr/hal00441977/fr/.
J. van der Hoeven. Newton's method and FFT trading. JSC, 45(8):857–878, 2010.
J. van der Hoeven. From implicit to recursive equations. Technical report, HAL, 2011. http://hal.archivesouvertes.fr/hal00583125/fr/.
A. Karatsuba and J. Ofman. Multiplication of multidigit numbers on automata. Soviet Physics Doklady, 7:595–596, 1963.
L.R. Rabiner, R.W. Schafer, and C.M. Rader. The chirp ztransform algorithm and its application. Bell System Tech. J., 48:1249–1292, 1969.
Alexandre Sedoglavic. Méthodes seminumériques en algèbre différentielle ; applications à l'étude des propriétés structurelles de systèmes différentiels algébriques en automatique. PhD thesis, École polytechnique, 2001.
A. Schönhage and V. Strassen. Schnelle Multiplikation grosser Zahlen. Computing, 7:281–292, 1971.
A.L. Toom. The complexity of a scheme of functional elements realizing the multiplication of integers. Soviet Mathematics, 4(2):714–716, 1963.